About the role
Role Overview:
We are seeking a highly qualified Music Domain Reviewer to support the quality assurance of Large Language Model (LLM) evaluation projects. In this role, you will review domain-specific prompts, evaluate completed tasks for accuracy and quality, ensure adherence to project guidelines, and provide actionable feedback to maintain high annotation standards. Your expertise in music will help ensure the reliability, consistency, and quality of AI evaluation data across diverse music-related topics.
Key Responsibilities
- Review and validate domain-specific prompts covering music theory, genres, composers, artists, music history, production, instruments, and related topics.
- Evaluate completed tasks to ensure factual accuracy, reasoning quality, completeness, and compliance with project guidelines.
- Identify factual inaccuracies, logical inconsistencies, hallucinations, outdated information, and low-quality annotations.
- Ensure prompts are challenging, relevant, and aligned with project objectives.
- Provide clear, constructive, and evidence-based feedback to contributors to improve task quality.
- Maintain consistency across reviews by following established quality standards and review guidelines.
- Escalate ambiguous or complex cases when necessary and document review findings.
- Collaborate with project managers and AI teams to continuously improve evaluation quality and review processes.
Minimum Qualifications
- Master's degree or higher in Music, Music Theory, Musicology, Ethnomusicology, Music Education, Music Performance, or a closely related discipline.
- Minimum 3 years of professional, research, teaching, or industry experience in the music domain.
- Prior experience reviewing, evaluating, editing, or quality-checking domain-specific content is preferred.
- Excellent written English communication and analytical skills.
- Strong attention to detail and the ability to identify factual, logical, and contextual errors.
Preferred Qualifications
- Experience reviewing AI-generated content, LLM evaluations, prompt engineering, or annotation quality.
- Familiarity with quality assurance, editorial review, or content evaluation workflows.
- Strong knowledge of music theory, genres, composers, artists, instruments, music production, and music history.
- Ability to provide objective, evidence-based feedback while maintaining consistent quality standards.
- Experience working independently in a fast-paced, quality-focused environment.
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